We proceed with the recipe as follows:
- The code is very similar to the prior recipe, except when we get to the loss function. We begin by loading the libraries; starting a session; loading the data; declaring the batch size; and creating the placeholders, variables, and model output, as follows:
import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from sklearn import datasets sess = tf.Session() iris = datasets.load_iris() x_vals = np.array([x[3] for x in iris.data]) y_vals = np.array([y[0] for y in iris.data]) batch_size = 50 x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) A = tf.Variable(tf.random_normal(shape=[1,1])) b = tf.Variable(tf.random_normal(shape=[1,1])) ...
